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Tumor Progression02:07

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Related Experiment Video

Updated: May 14, 2026

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
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Published on: September 19, 2019

Modeling tumor evolutionary dynamics.

Beatriz Stransky1, Sandro J de Souza

  • 1Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC São Paulo, Brazil.

Frontiers in Physiology
|February 20, 2013
PubMed
Summary
This summary is machine-generated.

Cancer development is an evolutionary process driven by genetic and epigenetic events. Mathematical models are crucial for testing assumptions about tumor initiation and evolution, advancing our understanding of cancer biology.

Keywords:
modelingmutationsomatic mutationstumortumorigenesis

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Area of Science:

  • Oncology
  • Evolutionary Biology
  • Computational Biology

Background:

  • Tumorigenesis is viewed as an evolutionary process involving discrete stages of genetic and epigenetic events.
  • Most cellular mutations are passengers, not directly contributing to cancer development.
  • Tumor evolution involves selection of advantageous driver mutations and clonal expansions.

Purpose of the Study:

  • To review mathematical and computational models of tumorigenesis.
  • To discuss the impact of these models on tumor biology.
  • To address uncertainties regarding the number and adaptive advantage of driver mutations in cancer initiation.

Main Methods:

  • Review of recent mathematical and computational modeling approaches.
  • Statistical and mathematical testing of cancer initiation and development hypotheses.
  • Analysis of high-quality cancer data.

Main Results:

  • Mathematical models provide a framework for testing fundamental assumptions in cancer biology.
  • These models help elucidate the roles of driver and passenger mutations in tumor evolution.
  • The review highlights the increasing utility of computational approaches in understanding tumorigenesis.

Conclusions:

  • Mathematical and computational models are essential tools for investigating the complex evolutionary process of cancer.
  • Further development and application of these models are needed to fully understand cancer initiation and progression.
  • This review underscores the interdisciplinary nature of modern cancer research, integrating biology with quantitative sciences.